Similarity quantification for linear stochastic systems as a set-theoretic control problem

For the formal verification and design of control systems, abstractions with quantified accuracy are crucial. Such similarity quantification is hindered by the challenging computation of approximate stochastic simulation relations. This is especially the case when considering accurate deviation bounds between a stochastic continuous-state model and its finite-state abstraction. In this work, we give a comprehensive computational approach and analysis for linear stochastic systems. More precisely, we develop a computational method that characterizes the set of possible simulation relations and optimally trades off the error contributions on the system's output with deviations in the transition probability. To this end, we establish an optimal coupling between the models and simultaneously solve the approximate simulation relation problem as a set-theoretic control problem using the concept of invariant sets. We show the variation of the guaranteed satisfaction probability as a function of the error trade-off in a case study where a formal specification is given as a temporal logic formula.Comment: 16 pages, 9 figures, submitted to Automatic

Impact of nationwide enhanced implementation of best practices in pancreatic cancer care (PACAP-1):a multicenter stepped-wedge cluster randomized controlled trial

Background: Pancreatic cancer has a very poor prognosis. Best practices for the use of chemotherapy, enzyme replacement therapy, and biliary drainage have been identified but their implementation in daily clinical practice is often suboptimal. We hypothesized that a nationwide program to enhance implementation of these best practices in pancreatic cancer care would improve survival and quality of life. Methods/design: PACAP-1 is a nationwide multicenter stepped-wedge cluster randomized controlled superiority trial. In a per-center stepwise and randomized manner, best practices in pancreatic cancer care regarding the use of (neo)adjuvant and palliative chemotherapy, pancreatic enzyme replacement therapy, and metal biliary stents are implemented in all 17 Dutch pancreatic centers and their regional referral networks during a 6-week initiation period. Per pancreatic center, one multidisciplinary team functions as reference for the other centers in the network. Key best practices were identified from the literature, 3 years of data from existing nationwide registries within the Dutch Pancreatic Cancer Project (PACAP), and national expert meetings. The best practices follow the Dutch guideline on pancreatic cancer and the current state of the literature, and can be executed within daily clinical practice. The implementation process includes monitoring, return visits, and provider feedback in combination with education and reminders. Patient outcomes and compliance are monitored within the PACAP registries. Primary outcome is 1-year overall survival (for all disease stages). Secondary outcomes include quality of life, 3- and 5-year overall survival, and guideline compliance. An improvement of 10% in 1-year overall survival is considered clinically relevant. A 25-month study duration was chosen, which provides 80% statistical power for a mortality reduction of 10.0% in the 17 pancreatic cancer centers, with a required sample size of 2142 patients, corresponding to a 6.6% mortality reduction and 4769 patients nationwide. Discussion: The PACAP-1 trial is designed to evaluate whether a nationwide program for enhanced implementation of best practices in pancreatic cancer care can improve 1-year overall survival and quality of life. Trial registration: ClinicalTrials.gov, NCT03513705. Trial opened for accrual on 22th May 2018

Impact of nationwide enhanced implementation of best practices in pancreatic cancer care (PACAP-1): A multicenter stepped-wedge cluster randomized controlled trial

Background: Pancreatic cancer has a very poor prognosis. Best practices for the use of chemotherapy, enzyme replacement therapy, and biliary drainage have been identified but their implementation in daily clinical practice is often suboptimal. We hypothesized that a nationwide program to enhance implementation of these best practices in pancreatic cancer care would improve survival and quality of life. Methods/design: PACAP-1 is a nationwide multicenter stepped-wedge cluster randomized controlled superiority trial. In a per-center stepwise and randomized manner, best practices in pancreatic cancer care regarding the use of (neo)adjuvant and palliative chemotherapy, pancreatic enzyme replacement therapy, and metal biliary stents are implemented in all 17 Dutch pancreatic centers and their regional referral networks during a 6-week initiation period. Per pancreatic center, one multidisciplinary team functions as reference for the other centers in the network. Key best practices were identified from the literature, 3 years of data from existing nationwide registries within the Dutch Pancreatic Cancer Project (PACAP), and national expert meetings. The best practices follow the Dutch guideline on pancreatic cancer and the current state of the literature, and can be executed within daily clinical practice. The implementation process includes monitoring, return visits, and provider feedback in combination with education and reminders. Patient outcomes and compliance are monitored within the PACAP registries. Primary outcome is 1-year overall survival (for all disease stages). Secondary outcomes include quality of life, 3- and 5-year overall survival, and guideline compliance. An improvement of 10% in 1-year overall survival is considered clinically relevant. A 25-month study duration was chosen, which provides 80% statistical power for a mortality reduction of 10.0% in the 17 pancreatic cancer centers, with a required sample size of 2142 patients, corresponding to a 6.6% mortality reduction and 4769 patients nationwide. Discussion: The PACAP-1 trial is designed to evaluate whether a nationwide program for enhanced implementation of best practices in pancreatic cancer care can improve 1-year overall survival and quality of life. Trial registration: ClinicalTrials.gov, NCT03513705. Trial opened for accrual on 22th May 2018

Characterising droughts in Central America with uncertain hydro-meteorological data

Central America is frequently affected by droughts that cause significant socio-economic and environmental problems. Drought characterisation, monitoring and forecasting are potentially useful to support water resource management. Drought indices are designed for these purposes, but their ability to characterise droughts depends on the characteristics of the regional climate and the quality of the available data. Local comprehensive and high-quality observational networks of meteorological and hydrological data are not available, which limits the choice of drought indices and makes it important to assess available datasets. This study evaluated which combinations of drought index and meteorological dataset were most suitable for characterising droughts in the region. We evaluated the standardised precipitation index (SPI), a modified version of the deciles index (DI), the standardised precipitation evapotranspiration index (SPEI) and the effective drought index (EDI). These were calculated using precipitation data from the Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS), the CRN073 dataset, the Climate Research Unit (CRU), ECMWF Reanalysis (ERA-Interim) and a regional station dataset, and temperature from the CRU and ERA-Interim datasets. The gridded meteorological precipitation datasets were compared to assess how well they captured key features of the regional climate. The performance of all the drought indices calculated with all the meteorological datasets was then evaluated against a drought index calculated using river discharge data. Results showed that the selection of database was more important than the selection of drought index and that the best combinations were the EDI and DI calculated with CHIRPS and CRN073. Results also highlighted the importance of including indices like SPEI for drought assessment in Central America.Universidad de Costa Rica/[805-B0-810]/UCR/Costa RicaUniversidad de Costa Rica/[805-A9-532]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-600]/UCR/Costa RicaUniversidad de Costa Rica/[805-B0-065]/UCR/Costa RicaUniversidad de Costa Rica/[805-B3-413]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-227]/UCR/Costa RicaUniversidad de Costa Rica/[805-B4-228]/UCR/Costa RicaUniversidad de Costa Rica/[805-B5-295]/UCR/Costa RicaUppsala University/[54100006]//SueciaMarie Curie Intra-European Fellowship/[No.329762]//EuropaUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias Básicas::Centro de Investigaciones Geofísicas (CIGEFI)UCR::Vicerrectoría de Docencia::Ciencias Básicas::Facultad de Ciencias::Escuela de Físic

Panta Rhei benchmark dataset: socio-hydrological data of paired events of floods and droughts

As the adverse impacts of hydrological extremes increase in many regions of the world, a better understanding of the drivers of changes in risk and impacts is essential for effective flood and drought risk management and climate adaptation. However, there is currently a lack of comprehensive, empirical data about the processes, interactions, and feedbacks in complex human–water systems leading to flood and drought impacts. Here we present a benchmark dataset containing socio-hydrological data of paired events, i.e. two floods or two droughts that occurred in the same area. The 45 paired events occurred in 42 different study areas and cover a wide range of socio-economic and hydro-climatic conditions. The dataset is unique in covering both floods and droughts, in the number of cases assessed and in the quantity of socio-hydrological data. The benchmark dataset comprises (1) detailed review-style reports about the events and key processes between the two events of a pair; (2) the key data table containing variables that assess the indicators which characterize management shortcomings, hazard, exposure, vulnerability, and impacts of all events; and (3) a table of the indicators of change that indicate the differences between the first and second event of a pair. The advantages of the dataset are that it enables comparative analyses across all the paired events based on the indicators of change and allows for detailed context- and location-specific assessments based on the extensive data and reports of the individual study areas. The dataset can be used by the scientific community for exploratory data analyses, e.g. focused on causal links between risk management; changes in hazard, exposure and vulnerability; and flood or drought impacts. The data can also be used for the development, calibration, and validation of sociohydrological models. The dataset is available to the public through the GFZ Data Services (Kreibich et al., 2023, https://doi.org/10.5880/GFZ.4.4.2023.001)

The challenge of unprecedented floods and droughts in risk management

Risk management has reduced vulnerability to floods and droughts globally1,2, yet their impacts are still increasing3. An improved understanding of the causes of changing impacts is therefore needed, but has been hampered by a lack of empirical data4,5. On the basis of a global dataset of 45 pairs of events that occurred within the same area, we show that risk management generally reduces the impacts of floods and droughts but faces difficulties in reducing the impacts of unprecedented events of a magnitude not previously experienced. If the second event was much more hazardous than the first, its impact was almost always higher. This is because management was not designed to deal with such extreme events: for example, they exceeded the design levels of levees and reservoirs. In two success stories, the impact of the second, more hazardous, event was lower, as a result of improved risk management governance and high investment in integrated management. The observed difficulty of managing unprecedented events is alarming, given that more extreme hydrological events are projected owing to climate change3

Skeletal Muscle Quality is Associated with Worse Survival After Pancreatoduodenectomy for Periampullary, Nonpancreatic Cancer

Body composition measures may predict outcomes of cancer surgery. Whereas low muscle mass shown on preoperative computed tomography (CT) scans has been associated with worse outcomes after surgery for pancreatic cancer, less consideration has been given to low muscle attenuation, reflecting poor muscle quality. Studies relating muscle mass and muscle attenuation with outcomes for patients with periampullary, nonpancreatic cancer are lacking. Skeletal muscle mass and attenuation were assessed in 166 consecutive patients undergoing pancreatoduodenectomy (PD) for periampullary, nonpancreatic cancer at a single center between 2000 and 2012. The skeletal muscle index (SMI) was calculated from cross-sectional muscle area on preoperative CT imaging at the third lumbar vertebra level (L3) and normalized for height. The skeletal muscle attenuation index (MAI) was calculated by measuring the average Hounsfield units of the total muscle area at the L3 level. Overall survival (OS) and the rate of major postoperative complications (Clavien-Dindo ≥3) were extracted from prospectively maintained databases. Low SMI was present in 78.3 % and low MAI in 48.8 % of the patients. The multivariate analysis showed lymph node metastasis [hazard ratio (HR) 1.8; 95 % confidence interval (CI) 1.1-2.9], microscopic radicality (HR 2.0; 95 % CI 1.2-3.4), and low MAI (HR 2.0; 95 % CI 1.2-3.3), but not low SMI to be significantly associated with decreased OS. Low MAI (HR 1.9; 95 % CI 1.0-3.8) was the only independent risk factor for major postoperative complications. Skeletal muscle quality, but not muscle mass, predicted survival and major complications after PD for periampullary, nonpancreatic cancer. Preoperative CT-derived body composition measures may stratify patients into risk categories and support shared decision makin